{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0b9ffbb2-8d33-eb0c-5a42-0733bdc3b74b",
    "_uuid": "e14f65723f5ac826104e861c45e58525740d8b2a"
   },
   "source": [
    "# Bikeshare数据集上的数据探索\n",
    "\n",
    "1、\t任务描述\n",
    "请在Capital Bikeshare （美国Washington, D.C.的一个共享单车公司）提供的自行车数据上进行回归分析。根据每天的天气信息，预测该天的单车共享骑行量。\n",
    "\n",
    "原始数据集地址：http://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset\n",
    "1)\t文件说明\n",
    "day.csv: 按天计的单车共享次数（作业只需使用该文件）\n",
    "hour.csv: 按小时计的单车共享次数（无需理会）\n",
    "readme：数据说明文件\n",
    "\n",
    "2)\t字段说明\n",
    "- Instant记录号\n",
    "- Dteday：日期\n",
    "- Season：季节（1=春天、2=夏天、3=秋天、4=冬天）\n",
    "- yr：年份，(0: 2011, 1:2012)\n",
    "- mnth：月份( 1 to 12)\n",
    "- hr：小时 (0 to 23)  （只在hour.csv有，作业忽略此字段）\n",
    "- holiday：是否是节假日\n",
    "- weekday：星期中的哪天，取值为0～6\n",
    "- workingday：是否工作日\n",
    "- 1=工作日 （是否为工作日，1为工作日，0为非周末或节假日\n",
    "- weathersit：天气（1：晴天，多云；2：雾天，阴天；3：小雪，小雨；4：大雨，大雪，大雾）\n",
    "- temp：气温摄氏度\n",
    "- atemp：体感温度\n",
    "- hum：湿度\n",
    "- windspeed：风速\n",
    "- casual：非注册用户个数\n",
    "- registered：注册用户个数\n",
    "- cnt：给定日期（天）时间（每小时）总租车人数，响应变量y （cnt = casual + registered）\n",
    "\n",
    "casual、registered和cnt三个特征均为要预测的y，作业里只需对cnt进行预测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导入必要的工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "2ba3154a-c2aa-3158-1984-63ad2c0c786a",
    "_uuid": "5eb696b95780825e94ddb49787f9fa339fc3833b"
   },
   "outputs": [],
   "source": [
    "# 数据读取及基本处理\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# plotting\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# setting params\n",
    "params = {'legend.fontsize': 'x-large',\n",
    "          'figure.figsize': (30, 10),\n",
    "          'axes.labelsize': 'x-large',\n",
    "          'axes.titlesize':'x-large',\n",
    "          'xtick.labelsize':'x-large',\n",
    "          'ytick.labelsize':'x-large'}\n",
    "\n",
    "sn.set_style('whitegrid')\n",
    "sn.set_context('talk')\n",
    "\n",
    "plt.rcParams.update(params)\n",
    "pd.options.display.max_colwidth = 600\n",
    "\n",
    "# pandas display data frames as tables\n",
    "from IPython.display import display, HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "21fa35be-878b-b4f2-ef6e-68dc070b8bfa",
    "_uuid": "73aee228226be55c0c8a6e4fcbf818c56cd94926"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train : (731, 16)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读入数据\n",
    "train = pd.read_csv(\"E:\\my work\\Bikeshare\\day.csv\")\n",
    "print(\"train : \" + str(train.shape))\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.4+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "没有缺失数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "be11c1c7-acfe-cb9a-bc81-95d461b884c5",
    "_uuid": "b53750ea6a3a2a02aa4297f5401e29c2f4d92e31"
   },
   "source": [
    "## 1.数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对数据值型特征，用常用统计量观察其分布\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 离散特征的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "season属性的不同取值和出现的次数\n",
      "3    188\n",
      "2    184\n",
      "1    181\n",
      "4    178\n",
      "Name: season, dtype: int64\n",
      "\n",
      "mnth属性的不同取值和出现的次数\n",
      "12    62\n",
      "10    62\n",
      "8     62\n",
      "7     62\n",
      "5     62\n",
      "3     62\n",
      "1     62\n",
      "11    60\n",
      "9     60\n",
      "6     60\n",
      "4     60\n",
      "2     57\n",
      "Name: mnth, dtype: int64\n",
      "\n",
      "weathersit属性的不同取值和出现的次数\n",
      "1    463\n",
      "2    247\n",
      "3     21\n",
      "Name: weathersit, dtype: int64\n",
      "\n",
      "weekday属性的不同取值和出现的次数\n",
      "6    105\n",
      "1    105\n",
      "0    105\n",
      "5    104\n",
      "4    104\n",
      "3    104\n",
      "2    104\n",
      "Name: weekday, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#对类别型特征，观察其取值范围及直方图\n",
    "categorical_features = ['season','mnth','weathersit','weekday']\n",
    "for col in categorical_features:\n",
    "    print '\\n%s属性的不同取值和出现的次数'%col\n",
    "    print train[col].value_counts()\n",
    "    train[col] = train[col].astype('object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "类别型特征的取值不多\n",
    "类别型特征可以采用独热编码（One hot encoding）/哑编码"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 数值特征的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000282E56BB2E8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000282E5F5F940>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000282E5F8A2B0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000282E56DFC18>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#对数值型特征，直方图\n",
    "numerical_features = ['temp','atemp','hum','windspeed']\n",
    "train[numerical_features].hist(figsize=(20, 12), bins=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 特征与目标之间的关系"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1 每年骑行量的分布\n",
    "violinplot中用x表示类别（年）信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10cba0a10>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
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4HKayshKHw8GQIUM6jf2q5xARkb6lo6ODpUuX\nsmjRIj7++GMsa9/dn9HkLEJ5o4lkFoHpSGCV3ZRhEPX3I+rvh9HRiqt2Le5d64lGQyxZsoQlS5aQ\nkZHB1KlTOeussxg2bJjW6hMREZFeYcKECcyaNYs5c+ZQ2erkoc/9XD+uGbc+MoocU4WFhfHvFIZh\nMGjQIAV8Il1gQ5OTR0r9RG2DwsJC7r77boV7IofRIwM+gNNPP52ysjL+8Y9/cN5553V6LRAIsGLF\nCgBKSkoYMGAAycnJrFixgoaGBjIyMjrtv2jRovgx95oyZQqffPIJ//znPw8I+D744AMCgQAnnnhi\nvLvvhBNOOOJziIhI72ZZFp999hmLFi3i3XffJRAIxF+zTSeRjEGEco/D8uWAAqmvxE5KITRwEqEB\nE3DVb8RVtwFH2y4aGhp46aWXeOmllxg8eDBnnXUWZ511Frm5uYkuWUREROQbOeOMM2hpaeGBBx5g\nXaOL36zxc/WYFhyaqUzkmKmqqsK27XgHX2VlZaJLEun1trY6eOAzPyHLIDc3l/vvv5/09PRElyXS\nrTlmz549O9FFfB1FRUW89NJLrFu3jvT09HgIFwqFmD17NitXrmTKlClcfPHFOJ1O6uvrWblyJRUV\nFUydOjU+jdfjjz/O3//+d8aOHcv06dPjxy8sLOQvf/kLn332GRMmTIhPBbpjxw5mzJhBc3Mzt956\nK4MHDwb4Wuf4MtXV1QwYMOBo/OcSEZEuYts2mzdv5uWXX2bu3Lm88sorbNy4kXA4jI1BNDWfjgET\nCBadSiSrGNvt63bhnhHpwF1bBkAobxQ4kxJc0UGYJpYvm3DOCMJZxdjOJMxQK0Y0RGNjI6tWreLl\nl1/m008/xbIs8vLySErqhu9DRERE5CsYMWIELpeLVatWsTPgoD5oMiE73N0+Ror0CguqvFRs20lZ\nWRnl5eW8/PLLrFixArdpcf6gYKLLE+mVattN7v4kjdawSVpaGg8//LCui4vscbicyLBtu8dO3r5k\nyRKuueYaOjo6GDp0KIMHD2bNmjVUV1czcOBA/vCHP9CvXz8AWltb+elPf8r69evJy8tj/PjxVFRU\nsH79ejIyMvjTn/5EcXFxp+P/9a9/5de//jWGYTBp0iR8Ph/Lli0jEAhw0UUXMWvWrE77f51zHM7K\nlSspKSn55v+hRETkmIpEIpSWlvLBBx+wdOlSdu7c2en1aHIm4ayhRDKLe8TaekawmZTSlwFoPf7f\nsT2pCa7oK7JtHK21OOs34tq9BSMair9kmiZjx45l8uTJnHrqqfHpu0VERER6Ctu2efzxx/nLX/4C\nwNT8IBcNb1PIJ3KUXfl+Bi3hA1tk/S6L30xpSEBFIr1bXbvJ3Z+ksivowOv18vDDDzNixIhElyXS\nbRwuJ+rRAR/Apk2b+O1vf8uHH35IU1MTeXl5nHXWWVx++eUHTJPZ2trK448/zptvvklNTQ25ubmc\ndNJJXHXVVeTn5x/0+P/617944oknKC0txbZtiouL+dnPfsYFF1xw0PV9vs45DkUBn4hI9xUIBPj4\n449ZunQpy5Yto6WlpdPrlstHOKuYSNZQrOSMQxyle+qxAd/+rCjOxq046zfhbNqKYVudXi4uLmby\n5MlMnjyZESNGaM0+ERER6RFs22bu3Lm88cYbAPzbgCAXj2jD1EcZkaNGAZ9I19m1J9yrCzpwuVzM\nnTuXCRMmJLoskW6lVwd8vZkCPhGR7qW+vp6lS5eydOlSVq1aRTgc7vR61JtBJL2QSHohli+72029\n+VX1ioBvf9EwzqZtOBuqYmHffp19ANnZ2fGwb/z48bjd7gQVKiIiIvLlotEoc+fOZeHChQCc3j/I\npSMV8okcLQr4RLpGTSAW7u3ucOB2u7nzzjuZNGlSossS6XYOlxM5u7gWERGRHqOjo4PS0lJWrlzJ\nypUrKS8v7/S6jUHUnxcP9Xp8ENZbOVxEMouIZBaBZeFo3RkL+xqrMEOt1NXV8eqrr/Lqq6/i9XqZ\nMGECJSUllJSUMGjQIHX3iYiISLficDiYOXMmDoeDBQsW8G61h4gNvzxOIZ+IiPQM1QGTe1al0RAy\n8Xg83HXXXUycODHRZYn0OAr4RERE9ohGo2zYsCEe6JWWlh7QpWebTiJpBXtCvQJwehJUrXwtpkk0\ndQDR1AF0FJ6E2b47HvY5AvW0t7fzr3/9i3/9618AZGVlxcO+iRMnkpOTk+A3ICIiIhJbX/j666/H\n6XTy2muvsXSnB8s2uPy4VhwHNh6JiIh0G9vbHNzzSSpNe8K9e++9l3HjxiW6LJEeSQGfiIj0WbZt\ns3379nigt2rVKlpbWw/YL+rNIJo6gEjqAKKp/cHUr89ewTCwkrMIJWcRyp+AEWrD2bQNR9MOHC07\nMCMd1NfXs2jRIhYtWgTAoEGDKCkp4YQTTmDcuHH4fL4EvwkRERHpq0zTZPr06TgcDv72t7/xYU0S\noajBr0a34HYkujoREZEDbW528MBnqbSETZKTk7n33ns5/vjjE12WSI+lK5QiItJn7A30SktL+fzz\nz1m1ahU1NTUH7Ge5fXvCvNjDdnkTUK10NdvtI5wzgnDOCLBtzMBuHM07cDbvwNGyE8OOUllZSWVl\nJa+88gqmaTJq1CjGjx/P8ccfz6hRo/D7/Yl+GyIiItKHGIbB1VdfjdPp5KWXXmJlnZu7P0llxtgW\n/G470eWJiIjEfVLn4jer/YQsA5/Px9y5cxk9enSiyxLp0RTwiYhIrxUOh9mwYQOlpaWsXr2a1atX\n09Bw4KLotsNNJLV/vEvPTkoFrbvWtxkGli8Ly5dFuP/xYEVwtO7C0bwdZ/MOzLZ6LMuK/38VG2JQ\nVFTE8ccfz5gxYzj++OPJy8vTGn4iIiJyTBmGwRVXXEF6ejq///3v2dTs4raVaVw/rpm8ZCvR5YmI\niLB4exLPrfdhY5CTk8O9995LcXFxossS6fEU8ImISK/R0tJCWVkZpaWllJaWsnbtWkKh0AH72aaT\naEouUX9/IqkDsHxZYGixEjkM00k0tT/R1P6EACIdOFuqcTTvwNFSg9neALbN5s2b2bx5M6+++ioA\n2dnZHH/88fFHUVERTqc+fomIiMjRZRgGF154IXl5edxzzz3UtMNtK9OYMa6FIamRRJcnIiJ9lGXD\ny5uTeb0yNjPSkCFDuPvuu8nNzU1wZSK9g64wiYhIj7R3us2ysjJWr15NaWkpFRUV2PaBUxFZrmSi\n/jyiKbGHlZyhQE++GWcSkYzBRDIGx/4c6cDRWoujtWbP8y4MO0pdXR1LlixhyZIlAHi9XkaPHs2Y\nMWMYM2YMI0aM0LSeIiIictRMnTqVrKwsZs2aRUtbG3evSuWK0S1MzAknujQREeljIhY8uTaFf9Uk\nAVBSUsJtt92mtexFjiIFfCIi0iPs2rWLdevWsX79+vhzS0vLAfvZgOXNiIV5e0I92+3TlJtybDmT\niKYPJJo+MPZnK4oZqI8Ffi2x4M+MBGlvb2fFihWsWLEiPnTgwIGMGDGCESNGMHLkSIYNG4bH40nQ\nGxEREZGebsKECcyfP5+ZM2dSW1vLvFI/PxoS4LzCoD4Si4hIl2jsMJi/2k95kwuAc845h+uvvx6X\ny5XgykR6FwV8IiLS7TQ1NXUK8tatW0d9ff1B97VNB1Ffzr5Az5cDzqQurljkC0wHVkouVkou4X6A\nbWN0NMe6+1pqYsFfsAmArVu3snXrVt56663YUNOkqKgoHviNHDmS4uJiTe0pIiIiX1lRURGPPfYY\nN954Ixs3buTFTT42Nzv5xXGtePWRQkREjqENTU4eLfXTGIrNnHTRRRdx2WWXaX16kWNAH+tERCSh\nAoEA5eXlncK86urqg+5rY2AlZxD1ZWP5cmLPngwwNd2mdHOGge1JI+JJI5I9LLYt0oGjrQ5HWx1m\n267YcziAZVls2rSJTZs2sWDBAgBcLhdDhw5l5MiR8eBv4MCBOByOBL4pERER6c6ys7N59NFHuf/+\n+3n77bdZviuJbW0Orjm+hQE+K9HliYhIL2Pb8Pb2JP64wUfUNvB4PNxwww18+9vfTnRpIr2WAj4R\nEeky9fX1bNq0iY0bN7Jx40Y2bdpEVVXVQdfNswHLk4bly4516PmysZIzwdSvLuklnElE0/KJpuXH\nNxmhAGZbHY62XTgCdZhtdZiRDsLhMGvXrmXt2rXxfb1eL0OHDmXo0KEMGTKEoUOHUlRURFKSOlhF\nREQkxuv1MmvWLEaOHMnjjz9OdcDJ7BVpXD6qjRNyQokuT0REeolQFJ5Z72PpzthyEwUFBdx+++0U\nFRUluDKR3k1XSUVE5KiLRqNs27YtHuTtfTQ0NBxyjOVO2dOZtzfQywKHuwurFkk8251M1F1INKNw\nzwYbI9QaC/za6vaEf3UYVoT29nZKS0spLS2NjzdNk8LCwnjwtzf8y8jISNA7EhERkUQzDIP/+I//\nYNiwYcyZM4eGhgYeKfXz3UHt/HtxAFMzpomIyDewq91kXqmfqtZY1DB58mRuuukmUlJSElyZSO+n\ngE9ERL6RQCDA5s2bO3Xlbd68mY6OjoPubwO2J5WoNxMrOYtociaWLxvb5e3awkV6AsPATvITSfIT\nySyObbMtzGBzLOwL1GMGduMI7MaIdmBZFhUVFVRUVMTX9APIyso6oNsvPz9fU3yKiIj0IePHj+eJ\nJ57g1ltvpaysjNcrvWxscvLfo1rJ8mjKThEROXIf17p5ep2PQMTEMAwuu+wyLrzwQkwtpSLSJRTw\niYjIVxKNRqmurmbLli1s2bIlPtXmjh07DjrFJoBtOrC8mbEQb2+Y580Ah6uLqxfpRQwTy5uO5U0n\nwtDYNtvGCAcwA/U4ArvjoZ/Z0QzEpsetr6/no48+ih/G4/FQXFzMkCFDKC4upqioiKKiItLS0hLx\nrkRERKQL5OTk8PDDD/Ob3/yGV199lXWNLmZ9nMalI9s4MVdTdoqIyFcTjMDzG3y8Xx2bktPv93PL\nLbdw4oknJrgykb5FAZ+IiHRi2za7du2KB3l7HxUVFYRCh/7Sb7m8WMmZRL1ZWMmZsYcnFQzdtSVy\nzBkGtttH1O0jml64b3s0vCfwq8ds3xP6BRow7CjBYJCysjLKyso6HSozMzMe9u19DB48mOTk5C5+\nUyIiInIsuN1upk+fzoQJE7j//vtpbW1l/mo/U/oHuWhYGx5dKRIRkcPY1Ozkt2tSqGmPzQgzfvx4\nbr75ZnJzcxNcmUjfo49tIiJ9WGNj4wFB3pYtW2hrazvkGNswsTxpWN6MWKCXHAv0NMWmSDfkcBH1\n5xH15+3bZluYwSbM/Tv92hswwwEAdu/eze7du1m5cmWnQ/Xr1++A4K+wsBC3W2tlioiI9ERnnHEG\no0aN4q677uLTTz/l/WoP5Y0ufjW6heLUaKLLExGRbsay4fVKL3/b4iVqGzgcDv7rv/6LH//4x1r+\nQSRBFPCJiPQBLS0tVFVVHRDkNTQ0HHKMDdhJqUS9GVjJGXumBMzASkoDzaUu0nMZZuzvsjcDsobs\n2x7pwNHegNneGAv82htwBBoworH1NHfu3MnOnTv58MMP40NM06SgoOCAbr/8/HycTn3MFBER6e5y\nc3N54IEH+POf/8zTTz9NTTvcvjKNHxQFOH9QENNIdIUiItId1AdNfleWwrrG2JIrBQUFzJo1i5Ej\nRya4MpG+TVdeRER6Cdu22b17N1VVVVRWVlJRURH/ub6+/rBjLZcPKzkW4EX3XPi3POng0K8JkT7D\nmUTU34+ov9++bbaNEWnHDOwL/hx7wj/DimBZFlVVVVRVVfHuu+/uO5TTSUFBAYWFhQwaNCj+KCws\nJCkpKQFvTkRERA7F4XBw4YUXUlJSwh133MG2bdt4abOPT+vd/OK4VvonW4kuUUREEsS24f3qJP60\nMZlAJHaz9/nnn8+VV16pZRxEugFduRUR6WEsy6KmpuagQV5LS8vhxzqT4p07ex9Rbzo4dcFdRA7C\nMLBdyUTTkomm5e/bbtsYodZ9gV9gT9dfsBHDtohEIlRUVFBRUfGFwxn069evU+i395GSktK1701E\nREQ6GTlyJE888QTz589nwYIFbGhyMevjdP69OMA5A9XNJyLS19QHTZ5Z5+Pz3bFlGfx+P9dffz2n\nn356gisTkb0U8ImIdFORSITt27dTVVVFRUUFlZWV8U6ZYDB42LGWKxnLm4blydjznI7lTcd2esDQ\nN3MR+YYMAzvJTzTJTzS9cN9228IItuAINsY6/uLPTRhWBNu2qa6uprq6mmXLlnU6ZFZW1kGDv4yM\nDAz9uyUiItIlkpOTueGGG5gyZQoPPPAAdXV1vLDRx8e1bn55XCsDfOrmExHp7Wwb3q1O4k8bkglG\nY117kydPZsaMGWRlZSW4OhHZnwI+EZEEa2lpYevWrVRVVXV63rZtG5FI5JDjYmvk+ePhXdSTFlsn\nz5MOTnfXvQERkb0ME9ubRsSbBhmD9m23bYxQ236BX+zZ0d4UX+Ovvr6e+vp6Vq1a1emQfr+fwsLC\n+GPgwIEUFhYyYMAArfMnIiJyjHzrW9/imWee4bHHHuONN95gU7OLW5an84OiAN8ZGMShJblFRHql\nuqDJ02t9rG6IXVdKTU3l6quv5swzz9SNlyLdkK6KiIh0gWg0Sk1NTTzA2//R0NBw2LG2YWJ5UuNB\nXuw5DcuTBqb+GReRHsAwsJNSiCalEE0r2LfdtjEiwf1Cv6Z9IWA4AMRuglizZg1r1qzpdEiHw8GA\nAQM6hX97A8DU1NSufHciIiK9kt/vZ+bMmZxxxhncd9991NXV8eImH8t3ufnlcW3k+6KJLlFERI4S\n24YlO5L480YfwWgsyJsyZQrXXnutuvZEujFdGRYROYoCgUA8uNu/G2/r1q2Ew+HDjrUdbixPLLjb\nO61m1JOO7fGDoVtkRaQXMgxsl5eoy0s0tX/n1yKhWNgXbIpP82kGmzA7mjFsm2g0Gv/3denSpZ2G\npqend+r22/tzv3791PUnIiJyhE466SSeffZZHn/8cf7xj3+wudnFLR+n8f8Mbue7g9px6auKiEiP\nVh0weXZdCmsbXUCsa+/aa6/l3/7t39S1J9LN6QqHiMgRikaj1NbWUlVVxbZt2zp15dXV1R12rE2s\niyUe5MWn1UzT+ngiIvtzurFScrFScjtvtyyMjhbMYBOOYCNGsAlHsAlzv+k+GxsbaWxs5PPPP+80\n1OVykZ+fHw/89j4KCwvx+/1d9c5ERER6nJSUFP7nf/6H008/nfvvv5/a2lr+tiWZj2rcXDqyjRHp\nh15aQEREuqeIBf+o9PJapZewFbseddpppzF9+nQyMjISXJ2IfBUK+EREDmHv2nh7H/uvjfel3Xim\na79OvL2PdCyPX9Nqioh8E2Zsnb+oN40ohZ1eMsLBfV1/e0I/M9iE0dGCgU04HKaiooKKiooDDpue\nnt4p8Nv7s9b6ExER2efEE0/k2Wef5amnnuKVV15hR8DJnavS+LcBQX40JIDPZSe6RBER+Qo2NDl5\nep2P7W2x7zrZ2dlcc801TJkyJcGViciR0NUKEenTIpEI1dXVB0ynuXXr1i9fGw+w3SlfmFYzFuTZ\nLq+68UREupjt8hB19SPq79f5BSuK2dEcD/xij0bMYDNGNATs6/orLS3tNHTvWn8DBw6koKCgU/iX\nkZGhKWtERKTPSU5OZtq0aZx55pncf//9bN68mSU7PHxS5+ai4W2ckBPSVyERkW6qPWLw0qZk3t6e\nhI2BYRhccMEF/OIXvyAlJSXR5YnIEVLAJyK9nm3bNDY2HjTE27FjB9Ho4ReHtx2uzlNqxh+p6sYT\nEekJTAeWNwPL+4VpZmwbIxLcL/Tb9zCCsa6//df6+yKfz3fQ6T7z8/NJSkrqojcnIiKSGKNGjeKJ\nJ57gxRdf5Nlnn6UxFObR1X4mZIf4+fA2sjxWoksUEZH9rNzl4n/LfTR0OAAYPHgw119/PWPGjElw\nZSLydenKtIj0GqFQiO3btx8wpWZVVRWtra2HHRtbG89/kG48rY0nItJrGQa2y0vU5T1I19++tf4O\neESCALS1tbF27VrWrl37hcMa5OXlxQO//UPA7Oxsdf2JiEiv4XQ6ufDCCzn99NN58MEHWbVqFZ/U\nuVnb4OLfiwNMLQhi6teeiEhC7e4web48mZW7YjchulwuLrroIn7yk5/gcrkSXJ2IfBMK+ESkR7Ft\nm927d3fqxNv7886dO7Gsw98lajk98e47e/9uvCQ/mI4uehciItLtdVrr7wsiHV8I/Zrjz4YdxbZt\ndu7cyc6dO1m+fHmnoV6v94COv70/ezyeLnt7IiIiR1NBQQEPPPAACxcu5LHHHqO5uZk/bPDxr51J\nXDqylUH+w8+aIiIiR59lw1vbPLy82UswagIwbtw4rrvuOgoLC79ktIj0BAr4RKRb6ujoYNu2bfEA\nb2+It23bNtra2g471jZMrKTUg3bj4dSUaSIi8g05k7BScrFScjtvty2MUFss7Gv/QtdfOABAe3s7\n5eXllJeXH3DY3NzcA4K/wsJCcnJyME2zK96ZiIjI12YYBt/5znc4+eSTeeyxx1i0aBGbW5zcuiKN\ncwYG+UFRgCTdUyki0iUqWxw8s97H5uZYh57f7+dXv/oV5557rmYUEelFFPCJSMLsXRuvqqqKysrK\nTs+1tbXYtn3YdOCZSwAAIABJREFU8ZbLe9C18eykFDB0IVRERLqYYWIn+Ykm+YmmFXR+LRruHPjt\nDQA7mjCsWFdDbW0ttbW1rFy5stPQpKQkCgoKKCwsZNCgQQwaNIjCwkIKCgq01p+IiHQ76enp3Hzz\nzZx99tk8+OCD7NixgzeqvCyvdXPx8DbGZYcTXaKISK/VEYVXtiSzcKsHy44FeVOnTuXKK68kIyPj\nS0aLSE+jgE9EjrloNMrOnTs7BXh7H83NzYcdaxsOLE/qQYM8nO4uegciIiLfkMOF5cvG8mV33m7b\n+7r+vvgIxTrWOzo62LRpE5s2beo01DAM+vfvH1/nb2/wN2jQIFJTU7vqnYmIiBzUCSecwDPPPMPz\nzz/PCy+8QF0QHvg8lRNzO/jZsDbSkw5/Q6eIiByZz+pdPLfeR10w1i49YMAAZsyYwQknnJDgykTk\nWFHAJyJHTUdHB1u3bu0U4lVWVrJ161bC4cPfpWk5vXum0kzvNK2m7U4BTR0gIiK9lWFgJ6UQTUoh\nmpbf+bVoGLOjeb/pPhvjP+9d62/Hjh3s2LGDZcuWdRqanp4eD/z2DwBzc3M13aeIiHSZpKQkfvGL\nX3DmmWfywAMPsHr1aj6uTWL1bhc/GRrgtP4d+ronIvINNYcM/rDBx7Ka2OweDoeDn/zkJ1x00UWa\n8UOkl1PAJyJHrK2tjYqKCrZs2dKpK2/nzp2HnVbTxsBO8seDvKgnDcubrrXxREREDsbhwkrOwkrO\n6rzdtjFCrZjtjQcEf2YkCEBjYyONjY189tlnnYZ6PB4GDhwYD/8GDx7M4MGDGTBgAE6nvhqIiMix\nUVRUxCOPPMLrr7/O7373O9ra2nhqXQof1iRx6YhW8pKtRJcoItLj2DZ8WOPmDxt8tIZjN/GNHj2a\n6667juLi4gRXJyJdQd/iReSQ2tvbqaysZMuWLWzZsoWKigoqKiqora097DjbdOzXibfn2ZOO5UkF\nU6uqi4iIfCOGsW+tPwZ2fikcjAV+waZOAaDR0YoBBINBNmzYwIYNGzqNc7lcFBYWUlRUFA/9Bg8e\nTP/+/XE49LtbRES+OdM0+d73vscpp5zCvHnzeP/99ylrcPHrj9P5YXGAswuCONRkLiLyldQFTZ5d\n7+Pz+tjyNV6vl8svv5wLLrhAM3aI9CEK+ESEjo4OKisr4115e5937tx52HGWMykW3HnT9zzHgjzb\n7dO0miIiIglguzxEXf2I+vt1fiEawexo2tPp17jvOdiEYVuEw+GDrvOXlJTEoEGDGDx4cKfwLy8v\nTxcORETka8nOzub222/n3Xff5eGHH6ahoYEXNsamlvuv41opTIkmukQRkW7LsmHx9iT+sslHMBq7\n9nbSSScxY8YM8vLyElydiHQ1BXwifUg4HKaqqioe4O0N86qrq7GsQ0+JYjvcRL3pWN6MWJjnzcDy\nZmA7PQryREREegKH8+DTfVrWnnX+GmIdf3ufg00Y2HR0dFBeXk55eXmnYR6P54Buv6KiInJycjD0\n2UBERL6C008/nYkTJ/L444+zYMECtrQ4uXV5GucXtvO9we241UAuItLJjjaTp9alsKHJBUBqairT\npk1j6tSp+gwu0kcp4BPppVpaWti4cWOnR2VlJZFI5JBjbNOJ5c3YL8yLBXq2K1lBnoiISG9kmntu\n3knvvN2KYgb3Bn+x0M8RbMAItmBgEwwGWbt2LWvXru00LDU1laFDh3Z6FBYWan0/ERE5KL/fzw03\n3MCZZ57JAw88wI4dO3itMpmVdW7+e1Qrg/3q5hMRsWxYuNXDy5uTCVux63NTp07lqquuIj09/UtG\ni0hvpm/aIj2cbdvs3LnzgDCvpqbm0GNMZ3xqzeh+XXmaWlNEREQAMB1YyRlYyRmdt1uRfev7tTfg\n2BP+GR0tGEBzczOrVq1i1apV8SEul4uioqJOoV9xcTEpKSld+55ERKTbKikp4amnnuKZZ57h5Zdf\nZnubkzkr0vj+4Ha+O6hda/OJSJ+1q93kibUprG+Mde3l5OQwY8YMvvWtbyW4MhHpDhTwifQgoVCI\nysrKA8K8tra2Q46x3D6iyVlYyZlYyZlEvZnYSX4FeSIiInLkzENM9RkNx7r82ndjBuoxA7txBHZj\nWBHC4fBBp/ns37//Ad1+ubm5ml5IRKSP8nq9XHHFFZx22mncdddd7Nixg79uSebTeheXH9dKf9+h\nl5UQEeltbBveq07ijxv2rbV3zjnnMG3aNN0oJyJxCvhEuqlIJEJFRUV8+qt169ZRWVlJNHrwKUps\nw8DyZMRCvORMrOQsosmZ4Ezq4spFRESkz3G4sFJysFJy9m2zbYyOFhz7BX5moB4zHACgurqa6upq\n3n///fgQv9/PsGHDGDlyJMcddxyjRo0iKyvri2cTEZFebMyYMTz55JP87ne/49VXX2VTs4tblqfz\n46EBzswPYuo+EBHp5Ro7DJ5el8Kn9W4A0tPTue6665gyZUqCKxOR7kYBn0g3UVdXx9q1aykrK6Os\nrIz169cTDAYPuq/tcO8J8TL3ded50sHUKuQiIiLSTRgGtieViCcVMov2bQ4HMfd0+sVCv92xaT6x\naWlpOWCKz7y8vHjYd9xxxzF8+HCSknQDk4hIb5acnMz06dM55ZRTuO+++6irq+P5ch+rdrn4xXFt\nZHnUzScivdPHtW6eXe+jNRybm3jy5Mlcf/31ZGRkfMlIEemLFPCJJEAwGKS8vJyysrJ4qLdr166D\n7ms73ERTcoj6cvZ05WVprTwRERHpsWyXh6hrANHUAYT3brQisXX9ArtxtNXhaNuFGdiNgU1NTQ01\nNTW88847ADgcDoYMGRIP/EaNGkVBQYGm9hQR6YVOOukknn76aebNm8fbb7/NmgY3v/7YyeXHtTIx\nJ/zlBxAR6SFCUXi+3Me71R4gdqPD1VdfzTnnnKPPuSJySAr4RLrArl27+OSTT1i9ejVr165l06ZN\nWNaBdxzahoHlzSSakkvUl0M0JQc7KVVhnoiIiPRuphPLl43lyyaSMzy2LRrBEajDbNuFo3VXLPQL\ntRGNRuNr+v3f//t/gdjUniNHjmTUqFGMHTuWMWPGqMtPRKSXSE1N5ZZbbmHy5Mk89NBDtLS08HBp\nKucVtvPvxQGcZqIrFBH5ZqoDJvNX+9naGrtUP378eG688Ub69euX4MpEpLtTwCdyDLS0tPDpp5+y\ncuVKVq1aRVVV1UH3s9wp8SAv6svB8mWBqb+WIiIiIjicRP39iPr7xTv9jFAAR1st5p7Az9FWh2FF\naGlpYfny5SxfvhwAt9vNmDFjKCkpoaSkhGHDhuFwaCpzEZGe7Nvf/jajR49mzpw5lJWVsaDKy4Ym\nJ1eObiVTU3aKSA/1UY2bp9b5CEZNDMPgkksu4Wc/+5k+u4rIV6IkQeQo6OjoYM2aNaxatYqVK1ey\nfv36Azr0bNNB1JdLNCUHa293nis5QRWLiIiI9Dy2O5mIezBkDN6zwcJsb4x197XuwtFaiyPYSCgU\niq/l9/vf/56UlBQmTJhASUkJEydOZODAgZrqSESkB8rLy2PevHk88cQTvPTSS2xocnHL8jT+e1Qr\nY7M0ZaeI9BxhC17Y4OOt7bEpOTMyMpg1axYlJSUJrkxEehIFfCJfQzQaZcOGDfFAr7S0lFAo1Gkf\nGyM2zVRqbI2ZaEoumLr7RkREROSoMUys5Eys5EzIGRHbFArgaKnG2bwDR/MOzFAbra2tvP/++7z/\n/vsA5OTkMHHixHiHX1ZWViLfhYiIHAGXy8WVV17J2LFjueeee2hpa+OBz/z8P4Pa+X+L2nFoyk4R\n6eZ2tZs8utpPRUvs0vy4ceO45ZZbyM7OTnBlItLTKOAT+Yps26asrIzFixezZMkSdu/efcA+UU86\n0dQBsVDP3w+c7gRUKiIiItJ32e5kIllDiGQNAdvG6GiOh33O5mqMaIhdu3axcOFCFi5cCMDo0aM5\n88wzOeOMM8jMzEzwOxARka9iypQpFBcXM2fOHMrLy3mtMpktLU6uGtOK12knujwRkYNa1+BkXqmf\ntkjsboQLL7yQSy+9FKdTl+lF5MjpXw6Rw7Btm82bN7N48WIWL15MdXV1p9ctl49o2gAi/v5EUwdg\nuzXlpoiIiEi3YRjYnjTCnjTCucfFpvQM7I4Hfo6WGgw7ypo1a1izZg3z589nwoQJnHnmmUyZMgW/\n35/odyAiIoeRn5/Po48+ymOPPcarr75K6W43d6xMZfq4FrK1Lp+IdDMfVLt5al0KUdsgNTWVm2++\nmZNPPjnRZYlID6aAT+Qgtm3bFg/1KioqOr0W9aQRySwmkjkYy5MOWr9FREREpGcwTCxfNiFfNvQf\nC1YER3M1rt2bcTZUYlkRVq5cycqVK3nooYc48cQT+fa3v80pp5yC1+tNdPUiInIQSUlJTJ8+neLi\nYubNm8fWNidzVqQxfWwzxanRRJcnIoJtw1+3eHmtItYYUFBQwD333ENBQUGCKxORnk4Bn8gelmWx\nZMkSXnrpJdatW9f5NbePSGYx4czi2BovCvVEREREej7TSTR9INH0gRCN4GzairN+M86mbYTDYZYu\nXcrSpUvxeDycdtppXHjhhQwaNCjRVYuIyEFccMEF9OvXjzlz5tAUCHDXqjT+v9GtnJATSnRpItKH\nhaLw5NoUltUmAbH19m677TbS0tISXJmI9AYK+KTPs22b999/n6effrpTt57l9BDJLCKSWUw0JVeh\nnoiIiEhv5nDu+exXBJEQzsZKXPWbcTTvIBgMsmjRIt566y3OOussfv7zn5Ofn5/oikVE5AtOOukk\n5s+fz4033khtbS2Plqbw46EBzh0Y1Fd6EelyzSGDeaV+NjS5ADj77LO5/vrrcbvdCa5MRHqLIwr4\n5s+fz8iRI5k6deph93vppZdYtWoVd9999zcqTuRYsm2bZcuW8fTTT7Nhw4b49kjaQEJ5o4im9gfD\nTGCFIiKJYQZ2x39OqvqI0IDxWCk5CaxIRKSLOd1EsocRyR6GEW7HuXsL7p2lEGpj4cKFvPXWW5x7\n7rlcdNFF5OXlJbpaERHZT3FxMY8//jg333wz69ev588bfTR2mPxkaEAhn4h0md0dJvd8ksrOgAOA\nyy67jIsuughD/xCJyFF0ROnF/PnzWbRo0Zfu9+6777JgwYKvXZTIsfbpp59y5ZVXctNNN8XDvUhq\nPm3HfZf24WcRTctXuCcifZLZugvvlvfif3Y1bSV5/ZuYrbsSWJWISOLYLi/hvFG0Hf/vBAtPxnJ5\niUajvP766/zsZz9j3rx5NDY2JrpMERHZT1ZWFvPmzePUU08F4M2tXv64IRnbTnBhItIn1AdN7loV\nC/ecTie33HILP//5zxXuichRd9gOvieffJJgMNhp2/r165k/f/4hx7S2tvLBBx/g8/mOToUiR9kb\nb7zB3Llzsfd8so/4+xHKn0jU3y/BlYmIJJ67Zg2GFem0zbDCuGvWEEw5IzFFiYh0B6aDcN4owjnD\ncdWuw139OeFwkL/97W98/PHHPPTQQ+Tm5ia6ShER2cPj8TB79mzuvvtu3n77bRZt8xK1DS4a3oap\na+wicozsao917u0KOnC5XNx+++2cfPLJiS5LRHqpwwZ8gUCAxx57LH53gWEYlJeXU15efsgxe0OT\nH/3oR0exTJGj47XXXuPBBx8EIOrNoGPgiURTB2h9PRGRPRytNUe0XUSkzzGdhPuNIZwzAndNGe4d\nn7B9+3auueYaHnzwQfr375/oCkVEZA+n08nNN9+Mw+Fg0aJFvL3dg2XDxSMU8onI0VfbbnL3qlTq\nOxy43W7uuOMOTjzxxESXJSK92GEDvssvv5xIJIJt29i2zZNPPsmwYcM444wzDrq/YRgkJSVRVFTE\nueeeeyzqFfnaXnnlFR555BEAor4cAsPPBmdSgqsSEelmrOiRbRcR6ascLkIDxhH1ZeHd8DbV1dVc\nc801PPTQQ+Tn5ye6OhER2cPhcDBz5kxM0+TNN99kyQ4PURsuG6mQT0SOnpqAyd2fpLK7w0FSUhJ3\n3XUXJSUliS5LRHq5wwZ8Ho+HGTNmxP+8YMECJk+ezHXXXXfMCxM5mt577714uBdJyaV9+NngcCe4\nKhERERHp6aJpBbQPPwvvhn9SW1vL9OnT+d///V88Hk+iSxMRkT0cDgc33HADDoeDf/zjH7xX7cFp\nwsXD2zShj4h8Y7uD+8I9j8fD3XffzYQJExJdloj0AYcN+L5o8eLFx6oOkWNqyZIlAESTs2gffg44\nXAmuSERERER6i2jqANqHnU3y+jeora2lrKyMiRMnJrosERHZj2maXHfddZimyd///ncWb/eQmWTx\nvcHtiS5NRHqwtrDB/Z/54+He3LlzGTt2bKLLEpE+4ogCvr3a2tqoqKigvb09vubewUyaNOlrFyZy\nNG3ZsgWASMYghXsiIiIictRFU/tjuX2Yodh3JQV8IiLdj2maXHvttTQ1NfHee+/x8uZkMpIspvTv\nSHRpItIDhS14pNTPtjYnpmkye/ZshXsi0qWOKOCzbZt7772XP/7xj0QikcPuaxgGZWVl36g4kaMh\nEomwbds2ACxvRoKrEREREZHeyvKmY4ba4jeXiYhI9+NwOPj1r39NQ0MDpaWlPL3OR7rb4viscKJL\nE5EexLLh92UprG2MNRJcd911nHzyyQmuSkT6miMK+J5//nmeffZZAHJycsjNzcXp/FpNgCJdJhAI\n7Auko6HEFiMiIiIivZNtY0RjF4ebm5sTXIyIiBxOUlISd955J9OmTaOyspJHV/u5eWITg/3RRJcm\nIj3Ei5uSWVabBMDFF1/M+eefn+CKRKQvOqJ07uWXX8Y0TR566CHOOeecY1WTyFGVmprKySefzLJl\ny0jatmLPNJ3uRJclIiIiIr2Ic/dmHK21APquJCLSA6SmpnLvvfdy5ZVXUl9fz4OfpXLbpEbSkw69\nFI2ICMCS7Um8UeUF4LzzzuOSSy5JbEEi0meZR7JzRUUFJSUl+sIqPc60adNwuVyY4XaStn+S6HJE\nREREpDeJhkja+jEA3/rWtzjllFMSXJCIiHwV/fr1495778Xj8dAYMnl0tZ+IleiqRKQ729jk5H/L\nfQBMmjSJGTNmYBhGgqsSkb7qiAK+lJQUfD7fsapF5JjJz8/npz/9KQCumjW4qz8HW3fliYiIiMg3\nY4QDeMsXYYbbcblcTJs2LdEliYjIERg6dCg33ngjABuaXPxpY3KCKxKR7qopZPDo6hSitsGAAQP4\nP//n/2j5Kvn/2bvv8KjK/P3j7zM9ySSkUIMQIYg06UVCFQUVdBUV21oAkaIiWEAxUgR09bfu7ldX\nV1ZdWbGsXXetq7AWJCBFpIaAlARCSyfJpEw5vz8icbOCCAInk9yv6/ISz3POnNvLOJl5Puf5PCKW\nOq4C37nnnsu6desoKSk5VXlETpnrr7+es846CwNw71mNZ/vnENQm2iIiIiJyYmwlB4nc9E8cP7Tm\nHD9+PImJiRanEhGR4zV48GCuu+46ABbvieDrfdrWoz5z2o78QPjRjkv9EAjB0xujKaiw4/F4mDdv\nHtHR0VbHEpF67rgKfHfddRfBYJAZM2aQn59/qjKJnBJut5snn3ySIUOGAOAs2EVk+vsY5UUWJxMR\nERGRcOM8uIXILR9h85fh8XiYPXs2o0aNsjqWiIicoFtuuYXu3bsDsDDDy65iu8WJxCptYwNHPH72\nUY5L/fD69ki2FDoBmDZtGsnJyRYnEhGB41pD/OKLL9KhQwcWL17Mf/7zH1q0aEGDBg2O2mf4tdde\nOykhRU6WiIgIZs6cSbt27ViwYAGUFRK1+V9UJHbH37gd2PQBXkRERESOzig/hHvPapwFuwBITExk\n/vz5tG7d2tpgIiLyqzgcDmbNmsWECRM4cOAAf94QzbzeRUQ6tGqrvrmoRRlrc11UBH+c7/TYQ1zY\noszCVGKllQdd/Ht3BABXX301559/vsWJRESqHFeB7+WXX67+czAYZNeuXUc9V5uLSm1lGAZXX301\nZ511FnPmzKGoqAjP7m9wHdxMxRm9CMQlgX5+RUREROS/BSpw7/0O58F0DDMEVG1hkJqaqvZMIiJ1\nRGxsLA899BCTJ08mpxz+viWKSR1LNEVQz7SOCTKhfTFPbowBoFtCBZe1KqN1TNDiZGKF3DIbL2yJ\nAqBLly6MHz/e4kQiIj86rgLfokWLTlUOkdOuW7duvPDCC/ztb3/j448/xlZRTMT2/xD0Nqa8RW9C\n3sZWRxQRERERq4WCOA+m4977HUawEoD4+HjGjh3L8OHDsdmOa9cDERGp5dq1a8f48eN5+umnWXHQ\nTad4PwMTK6yOJadZC++Pxbzrz/LRJDJkYRqxSjAEz2z24gvYiImJITU1FYfjuKbTRUROqeP6Ntq7\nd2969uzJzp07Wb58Ob17967+q6ysjD/+8Y9s3769+phIbZeQkMD06dN5/vnn6dWrFwD2koNEpX+A\n5/v/YPPlWZxQRERERCwRCuLI3UbUxnfw7F6JEazE4/EwevRoXn75ZS655BIV90RE6qirrrqKc889\nF4BFW6PYW6r3e5H66N1dEWwrqtp377777qNxYy0GEJHa5bg+oVRWVnLrrbcyZ84cPv744xpj2dnZ\nfPfdd8ydO5fJkycTDGrZuoSP5ORkfv/73/P//t//o1WrVgA4C3YRtemfRGz5GHvhbjDVd19ERESk\nzgtU4Nq3jqj1bxKxcym2imIMw2D48OG8/PLLjB49msjISKtTiojIKWQYBvfffz/x8fFUhgz+sima\nSk1zidQr6QUO3t9Vte/eyJEj6devn8WJRER+6rgKfK+88grLli2jY8eOPPTQQzXGrr/+et544w3O\nOeccFi9ezEsvvXRSg4qcDr179+b5559n2rRpNG/eHABH8T4it31G5MZ3ceZkQChgcUoREREROdmM\n8kO4M5fjXfc67j1rsPl9GIZB//79ef7555k+fToNGza0OqaIiJwmsbGxpKamYhgGWSUO3tyhhztE\n6osSv8GCzV5MDJKTk5k4caLVkUREjui4mgb/85//JD4+nhdffJGoqKifjHfu3JnnnnuOYcOG8fbb\nbzN69OiTlVPktLHb7YwYMYKLLrqI5cuX88Ybb7B+/Xrs5YXYdy3DtWcN/sbt8Dduj+mMsDquiIiI\niJwo08RecgDn/k04CjMxfjjs8Xi46KKLuOqqqzjjjDMsjSgiItbp0aMH1113Ha+++ir/3h1BlwQ/\nneL9VscSkVPINOHvGVEUVNhxu93MmjULt9ttdSwRkSM6rgJfVlYWKSkpRyzuHdagQQO6detGWlra\nrw53PEKhEDfffDMrV65k0aJF9OnTp8Z4aWkpCxcu5KOPPiI7O5vo6GgGDBjA5MmTSUxMPOJrrlmz\nhgULFrB582ZKS0tJTk7muuuu46qrrjri+SdyD6m97HY7/fv3p3///mzZsoXXX3+dL7/8EgLluPd+\nh2vfOgKxLfE3bEuwQXMw1JNfREREJBwY/jIcedtx5mzFXl5YfTwhIYErrriCSy+9lJiYGAsTiohI\nbTFmzBhWr17N1q1beW6zl4f7FOJ1agsPkboqbb+LlQerCnqTJk0iKSnJ4kQiIkd3XAU+j8dDSUnJ\nMc8LBAK4XK4TDnUinn/+eVauXHnEsbKyMsaOHct3331H8+bNGTRoEDt37uSdd95hyZIl/OMf/yA5\nObnGNZ988gl33XUXNpuNXr164fF4+Oabb0hNTWX9+vXMnTv3V99Dwke7du2YPXs2+/fv55133uGD\nDz7A5/PhLMjEWZBJyBmFv9FZ+BuehemOtjquiIiIiPwv08R+aC/OnAwchVkYZqh6KDk5mVGjRjFk\nyJDT/j1GRERqN6fTSWpqKuPHj6egooK/Z0Rxe8cSDOPY14pIeMkps/Hi1qqFLX369OGyyy6zOJGI\nyM87rgJfhw4dWLFiBdu3bz9qsWr37t2sXLmSrl27npSAv8TGjRt58sknjzr+1FNP8d1333HRRRfx\n+OOP43Q6AfjLX/7CE088wf3338+bb75ZfX5+fj4zZszA5XKxcOFCunfvDkB2djY33XQTr7/+OkOG\nDGHw4MEnfA8JT02bNuW2225jzJgxfPHFF3z44Yds3LgRm7+0alXf3u8IxiTib9SWQGwS2OxWRxYR\nERGp14yKEpy523DmbsNW+ePDih6PhyFDhjBixAg6dOiAoZlaERE5iqSkJCZOnMgTTzzByoNuuiVU\n0q9ZpdWxROQkCpnw7GYv5UEbDRo0YPr06fp8KCK13nH1FLz++usJBAKMHTuWjz76CJ/PVz1WVlbG\np59+yujRo/H7/Vx//fUnPeyRlJWVce+99xIbG8uZZ575k/GSkhJeffVVXC4Xs2fPri68Adx22210\n6tSJ9evXs3r16urjr7zyCj6fj2uvvba6uAfQvHlzHnzwQQAWLlz4q+4h4S0iIoKLL76Yp556ihdf\nfJGrr76aBg0aYACOQ3uJ2P4FUetew70rDXvxgaoG3iIiIiJyegQrceRuIyLjE6LWv4F779rq4l6H\nDh2YNm0a77zzDtOnT6djx46avBERkWO6/PLL6d27NwAvbo0ip0zbdIjUJR9mesgoqprTvffee0lI\nSLA4kYjIsR3XCr4hQ4Zw0003sWjRIu655x4MwyA6uqodYXFxMaZpYpomN9xwAxdddNEpCfy/fve7\n37Fz506ee+45nnrqqZ+Mr1q1Cp/Px7nnnkt8fPxPxocNG8bGjRv5/PPP6dmzJwCff/45AEOHDv3J\n+f379ycyMpJVq1ZRWlpKVFTUCd1D6o6kpCRuu+02br31VtLS0vjwww9ZtWoVtkAFrpwtuHK2EHJ5\n8Se0JpCQTCgizurIIiIiInVPKIi9KBtn3vYfWnAGq4diYmIYNmwYw4cPp3Xr1haGFBGRcGU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ps2bUpKSgopKSl07txZbYtEREROgejoaB566CHuuOMOcsphwWYvd3UuxqZnYkVOuuxSO3/b4gWq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\nM9xutyZNmqRJkyapvLxcq1ev1ttvv63t27fLDNbKX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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cb662d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.violinplot(data=train[['yr', 'cnt']], x=\"yr\",y=\"cnt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2011年和2012年的分布差异很大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2 一年中每天的骑车量\n",
    "用颜色参数hue表示类别（年）信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'dayly distribution of counts')]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "train['date'] = pd.to_datetime(train['dteday'])\n",
    "train['dayofyear'] = train[\"date\"].dt.dayofyear  #减今年的第几天\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(20, 12))\n",
    "sn.pointplot(data=train[['dayofyear', 'cnt', 'yr']], x='dayofyear', y='cnt', hue='yr',ax=ax)\n",
    "ax.set(title=\"dayly distribution of counts\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "每年开始和结束的数量少，中间多，骑行量和季节/月份有关"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.3 季节与骑车数量的关系\n",
    "violinplot得到详细分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x282e99ca4e0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20, 12))\n",
    "sn.violinplot(data=train[['season', 'cnt']], x=\"season\", y=\"cnt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "能看出来每个季节骑行量的分布不同\n",
    "barplot利用矩阵条的高度反映数值变量的集中趋势，以及使用errorbar功能（差棒图）来估计变量之间的差值统计。\n",
    "谨记barplot展示的是某种变量分布的平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Seasonly distribution of counts')]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20, 9))\n",
    "sn.barplot(data=train[['season', 'cnt']], x=\"season\", y=\"cnt\")\n",
    "ax.set(title=\"Seasonly distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "骑行量和季节的关系就很明显了：第2、3、4季度的骑行量明显高于第1季度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.4 月份与骑车数量的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,u'Monthly distribution of counts')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18792290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sn.barplot(data=train[['mnth', 'cnt']], x=\"mnth\", y=\"cnt\")\n",
    "ax.set(title=\"Monthly distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.5 天气和骑车数目的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'weathersit distribution of counts')]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(15, 10))\n",
    "sn.barplot(data=train[['weathersit', 'cnt']], x=\"weathersit\", y=\"cnt\")\n",
    "ax.set(title=\"weathersit distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.6 工作日和节假日的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x282ea50b198>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A3lj3CBkopXwx3dKWi5K8LsnPJLmx1vqdwTG11i2llK1JDk4y2CfkiiWXuqrv/54kG3fSv9sWFhZSa70rp+7Upk2bVvyaMC22bt2ayy+/fNLD2GWbNm2yYzQza1Sfx4WFhRW/JgDAOE0kCEnyzCT3SLdx6ofSbYy6bZnjtiXZkC7kyDLH7Go/AEASm7cDQOsmEoTUWj+XJKWUZyf5bJJ/zPKBxYYkW5PcNPT8+jvpX+783bZ27dqUUu7Kqbtgy4iuC5O1cePGEX5uRuPmSQ8ARmRUn8fNmzev6lkhQ5u3fzbd5u33SRd2bE/yO7nt5u17JXl333dCf/5g8/ZnJbk2yTvSbd7+lL5/sHn7ryb5cpLXptu8/cdqrYtjeZMAwE6N864xB5dSfmlJ8xf6x21J9i2l7Dd0/P757nKXwbcoBy05/6C+/9p0gccd9QMAJDZvB4DmjXN5fEnywVLK8J4dj0iymOTPk9yY5Iihvsf0bRfXWq9OculwfynlkCQHJDm//4bls0v675nkoUnOG8m7AQBWHZu3AwDjXBpzTpKLk5xRSnlxknulm076jlrrZaWU05OcVko5OslckjcleXOtdTBz/dQkJ5VSLktydZLTk5xZa/16339KkveXUi5JsjndJqz/Ums9d0zvDwBYRWzeDrNntW3cnti8ndk2rZu3j/P2ubck+YV035r8Q5K/SPLRJC/pD3llkk8l+XC6DVTPSrfR2MCpSd6abkOzv03ypSTPG7r+WUlekeQ1Sc5PV6z84qjeDwCw6j0zyU+lCy8+1D/avB0AZtxYN0vtd0x/8h30bUtyTP+zXP9iklf1P3d0/dOSnLbnIwUAZp3N223ezuxZjRu3JzZvZ3ZN6+btZmEBAM2weTsAIAgBAFpi83YAaNxYl8YAAEyYzdsBoHFmhAAwUmvnknvt/d01nPfeez5r5yY4IJpm83YAwIwQAEZqbi555oOuz7u/0m278IwH3ZA5QQgTZPN2AGibIASAkTvkHtvzusOvnfQwAADA0hgAAACgHYIQAAAAoBmCEAAAAKAZghAAAACgGYIQAAAAoBmCEAAAAKAZghAAAACgGYIQAAAAoBmCEAAAAKAZghAAAACgGYIQAAAAoBmCEAAAAKAZghAAAACgGYIQAAAAoBmCEAAAAKAZghAAAACgGYIQAAAAoBmCEAAAAKAZghAAAACgGYIQAAAAoBmCEAAAAKAZghAAAACgGYIQAAAAoBmCEAAAAKAZghAAAACgGYIQAAAAoBmCEAAAAKAZghAAAACgGYIQAAAAoBmCEAAAAKAZghAAAACgGYIQAAAAoBmCEAAAAKAZghAAAACgGYIQAAAAoBmCEAAAAKAZghAAAACgGYIQAAAAoBmCEAAAAKAZghAAAACgGYIQAAAAoBm7HISUUi4tpdxzmfb7lFKuWdlhAQDcnnoEANhT63bWWUp5QpJH90/vn+R3Sylblxz2wBGMCwAgiXoEAFhZOw1Cklyc5KVJ5vrnP5Jk+1D/YpIbkhy98kMDAEiiHgEAVtBOg5Ba62VJfjJJSil/muQltdYt4xgYAECiHgEAVtadzQi5Va31OaWUdaWUByZZn+9+KzPo/9JKDw4AYJh6BADYU7schJRSfj7Jnya5Z5YUHemmpK5dwXEBANyOegQA2FO7HIQkOTnJOUl+N4npqADAJKhHAIA9sjtByAOTPLXW+q+jGgwAwJ1QjwAAe2TNbhz7z0kOGdVAAAB2gXoEANgjuzMj5C+TvKOUcmSSr+S2t61LrfUtKzkwAIBlqEcAgD2yO0HIy5Jcl+Rx/U/SbUo21z8qPACAUVOPAAB7ZHeWxjwoyZ8l+cNa6w/UWn8gyX+m27n9QaMYHADAEuoRAGCP7E4Q8tokT0/y70Ntb05ydJKTVnJQAAB3QD0CAOyR3QlCjkry9FrrpwcNtdYz0hUez17hcQEALEc9AgDskd0JQvZJcsMy7d9OcveVGQ4AwE6pRwCAPbI7QcjfJfmjUso9Bg2llAOS/H6Sv1/hcQEALEc9AgDskd25a8yLk/xNkitKKZel25n9+5N8NckTRzA2AICl1CMAwB7Z5SCk1vr1UspDk/xUkh9Ksj3JV5J8qta6Y0TjAwC4lXoEANhTuzMjJLXW7Uk+2v8AAIydegQA2BO7s0cIAAAAwKomCAEAAACaIQgBAAAAmiEIAQAAAJohCAEAAACaIQgBAAAAmiEIAQAAAJohCAEAAACaIQgBAAAAmiEIAQAAAJohCAEAAACaIQgBAAAAmiEIAQAAAJohCAEAAACaIQgBAAAAmiEIAQAAAJohCAEAAACaIQgBAAAAmiEIAQAAAJohCAEAAACaIQgBAAAAmiEIAQAAAJohCAEAAACaIQgBAAAAmiEIAQAAAJohCAEAAACaIQgBAAAAmiEIAQAAAJohCAEAAACaIQgBAAAAmiEIAQAAAJohCAEAAACaIQgBAAAAmrFunC9WSjk4yR8nOTLJfJKPJjm+1npdKWX/JG9O8gtJbk7yliS/V2td7M9dl+T1SZ7Wj/s9SU6otW4fuv6JSV6YZL8kZyU5rtZ63ZjeHgAAADDlxjYjpJSyNl04cbd0QcgTkhya5N39Ie9M8sAkj03yvCQvTRdqDJyc5HH9eU/qH08euv6xSV6c5Dn99R/SXxMAAAAgyXiXxvxIkocneU6t9ZJa6z+nCy6eUEr5/iRPSfL8WuvmWuuHk/xOkpclSSll7yTHpps9cn6t9ewkL0pyTClln/76xyd5da31k7XWC5McneTJpZT7jfE9AgAAAFNsnEtjvpbkcbXWq4faFvvHH0/ynVrrJUN9Zyc5tZRyUJLvT7JvknOW9O+b5NBSyteSPGC4v9Z6SSnl20keleTrK/1mAIDVyVJdAGjb2IKQWuu1ST6xpPllSb6a5L5JrljSd1X/eHDff2Ot9TtD19tSStna9w+Kj+WucfBdGe/CwkJqrXfl1J3atGnTil8TpsXWrVtz+eWXT3oYu2zTpk12jGZmjerzuLCwsOLXHKehpbrXpAtC9kny1nRLdZ+YblntpnRLdTf17d9Kclp/ieGlunv1/duTnNBff7BU91lJrk3yjv6aTxn1ewMAds3E/g1QSvmNJE9OtxfIxiTblhwyeL7hDvoHxwz6s8wxg34AgMRSXQBo3ljvGjNQSnlVkpOSvKTW+tFSyg/l9oHF4PnWJDct0z84ZtA/eH79Mv27be3atSml3JVTd8GWEV0XJmvjxo0j/NyMxs2THgCMyKg+j5s3b17ts0Is1QWAxo09CCmlvDHdNy/H1lrf2jd/I8lBSw4dPL8y3dTTfUsp+9Var++vs3+6mSBX9OcPzvmvJddYulwGAGiUpbodS3WZVattmW5iqS6zbVqX6o71M1dKOSndFNKjh0KQJDkvyT1KKQ8ZantMkkv7b2wuTnJjkiOW9N+Y5OL+mEuH+0sphyQ5IMn5o3gvAMDqZ6kuALRnbDNCSik/kuTEJK9L8ulSyoFD3Vcm+askZ5RSXpBuJsfv9sen1npTKeX0JKeVUo5OMpfkTUneXGsdzGw/NclJpZTLklyd5PQkZ9ZaTUMFAG7HUl1LdZk9q3GZbmKpLrNrWpfqjnNpzJPTzUD59f5n2CFJfiXJ29Ktq92S5A1LZo28Mt3O7h9Od6u796YPSnqnJvneJO9Ksj7JR9JtaAYAcBuW6gJAu8Z5+9zfSvJbd3LYU3dy/rYkx/Q/y/UvJnlV/wMAsKwlS3XfM9R161LdWuuX+rZbl+qWUr6T7y7V/dhQ/2Cp7s2llMFS3Uv617JUFwCmzETuGgMAMAmW6gIAghAAoCWW6gJA4wQhAEAzLNUFANyyGgAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABohiAEAAAAaIYgBAAAAGiGIAQAAABoxrpJvGgpZS7Jx5J8tNZ62lD7iUlemGS/JGclOa7Wet1Q/3OTnJjk+5J8JskxtdZvDPU/IckfJPmBJBf2/V8c/TsCAAAAVoOxzwgppaxJ8qYkP7uk/dgkL07ynCRHJnlIkncO9f9ckjcmeWWSw5OsT/KXfaiSUsrDknwgyWlJDkvytSSfKKXsM+K3BAAAAKwSY50RUkp5QJJ3J9mU5Lol3ccneXWt9ZP9sUcn+Xwp5X611q/3/W+vtX6g739akquSPDrJuelClI/VWt/S9/9qksuTPCX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mu3XvHlO/wGgIQoD/384d0wAIBUEUXAs4QAsasIMKkq+X4jd0kBCqnVFxebm7FluSI8n5dlUWAODBSLJm/jpbMs949vuGyEfmF/iB0xgAAACghmepAAAAQA0hBAAAAKghhAAAAAA1hBAAAACghhACAAAA1BBCAAAAgBoXEz3eRYrA/uMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1,ax2) = plt.subplots(ncols=2, figsize=(18, 10))\n",
    "sn.barplot(data=train,x='holiday',y='cnt',ax=ax1)\n",
    "sn.barplot(data=train,x='workingday',y='cnt',ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.7 一周中哪一天的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'weathersit distribution of counts')]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 10))\n",
    "sn.barplot(data=train[['weekday', 'cnt']], x=\"weekday\", y=\"cnt\")\n",
    "ax.set(title=\"weathersit distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.8 数值型特征和y之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x282eb2a00f0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x792 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(11, 11))\n",
    "corrMatt = train[[\"temp\", \"atemp\", \"hum\", \"windspeed\", \"casual\", \"registered\", \"cnt\"]].corr()\n",
    "mask = np.array(corrMatt)\n",
    "mask[np.tril_indices_from(mask)] = False\n",
    "sn.heatmap(corrMatt, mask=mask, vmax=.8, square=True, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "体感温度和温度高度相关\n",
    "目标cnt与温度正相关、湿度和风速负相关"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.特征工程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 根据前面对数据的分析，首先对类别型（季节/月份/星期/天气）特征进行独热编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train_categ:  (731, 26)\n"
     ]
    },
    {
     "data": {
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   season_1  season_2  season_3  season_4  mnth_1  mnth_2  mnth_3  mnth_4  \\\n",
       "0         1         0         0         0       1       0       0       0   \n",
       "1         1         0         0         0       1       0       0       0   \n",
       "2         1         0         0         0       1       0       0       0   \n",
       "3         1         0         0         0       1       0       0       0   \n",
       "4         1         0         0         0       1       0       0       0   \n",
       "\n",
       "   mnth_5  mnth_6  ...  weekday_0  weekday_1  weekday_2  weekday_3  weekday_4  \\\n",
       "0       0       0  ...          0          0          0          0          0   \n",
       "1       0       0  ...          1          0          0          0          0   \n",
       "2       0       0  ...          0          1          0          0          0   \n",
       "3       0       0  ...          0          0          1          0          0   \n",
       "4       0       0  ...          0          0          0          1          0   \n",
       "\n",
       "   weekday_5  weekday_6  weathersit_1  weathersit_2  weathersit_3  \n",
       "0          0          1             0             1             0  \n",
       "1          0          0             0             1             0  \n",
       "2          0          0             1             0             0  \n",
       "3          0          0             1             0             0  \n",
       "4          0          0             1             0             0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categ_feats = ['season', 'mnth', 'weekday', 'weathersit']\n",
    "\n",
    "for categ in categ_feats:\n",
    "    train[categ] = train[categ].astype('object')\n",
    "    \n",
    "X_train_categ = train[categ_feats]\n",
    "X_train_categ = pd.get_dummies(X_train_categ)\n",
    "print('X_train_categ: ', X_train_categ.shape)\n",
    "X_train_categ.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 对数值型特征进行标准化/MinMaxScaler，去量纲"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.355170</td>\n",
       "      <td>0.373517</td>\n",
       "      <td>0.828620</td>\n",
       "      <td>0.284606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.379232</td>\n",
       "      <td>0.360541</td>\n",
       "      <td>0.715771</td>\n",
       "      <td>0.466215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.171000</td>\n",
       "      <td>0.144830</td>\n",
       "      <td>0.449638</td>\n",
       "      <td>0.465740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.175530</td>\n",
       "      <td>0.174649</td>\n",
       "      <td>0.607131</td>\n",
       "      <td>0.284297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.209120</td>\n",
       "      <td>0.197158</td>\n",
       "      <td>0.449313</td>\n",
       "      <td>0.339143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       temp     atemp       hum  windspeed\n",
       "0  0.355170  0.373517  0.828620   0.284606\n",
       "1  0.379232  0.360541  0.715771   0.466215\n",
       "2  0.171000  0.144830  0.449638   0.465740\n",
       "3  0.175530  0.174649  0.607131   0.284297\n",
       "4  0.209120  0.197158  0.449313   0.339143"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\"casual\", \"registered\"不考虑\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "mm_X = MinMaxScaler()\n",
    "num_feats = ['temp', 'atemp', 'hum', 'windspeed']\n",
    "tmp = mm_X.fit_transform(train[num_feats])\n",
    "X_train_num = pd.DataFrame(data=tmp, columns=num_feats, index=train.index)\n",
    "X_train_num.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 将两类数据通过concat()进行合并"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train:  (731, 32)\n"
     ]
    },
    {
     "data": {
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "      <th>mnth_1</th>\n",
       "      <th>mnth_2</th>\n",
       "      <th>mnth_3</th>\n",
       "      <th>mnth_4</th>\n",
       "      <th>mnth_5</th>\n",
       "      <th>mnth_6</th>\n",
       "      <th>...</th>\n",
       "      <th>weekday_6</th>\n",
       "      <th>weathersit_1</th>\n",
       "      <th>weathersit_2</th>\n",
       "      <th>weathersit_3</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
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       "      <td>0.144830</td>\n",
       "      <td>0.449638</td>\n",
       "      <td>0.465740</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   season_1  season_2  season_3  season_4  mnth_1  mnth_2  mnth_3  mnth_4  \\\n",
       "0         1         0         0         0       1       0       0       0   \n",
       "1         1         0         0         0       1       0       0       0   \n",
       "2         1         0         0         0       1       0       0       0   \n",
       "3         1         0         0         0       1       0       0       0   \n",
       "4         1         0         0         0       1       0       0       0   \n",
       "\n",
       "   mnth_5  mnth_6  ...  weekday_6  weathersit_1  weathersit_2  weathersit_3  \\\n",
       "0       0       0  ...          1             0             1             0   \n",
       "1       0       0  ...          0             0             1             0   \n",
       "2       0       0  ...          0             1             0             0   \n",
       "3       0       0  ...          0             1             0             0   \n",
       "4       0       0  ...          0             1             0             0   \n",
       "\n",
       "       temp     atemp       hum  windspeed  holiday  workingday  \n",
       "0  0.355170  0.373517  0.828620   0.284606        0           0  \n",
       "1  0.379232  0.360541  0.715771   0.466215        0           0  \n",
       "2  0.171000  0.144830  0.449638   0.465740        0           1  \n",
       "3  0.175530  0.174649  0.607131   0.284297        0           1  \n",
       "4  0.209120  0.197158  0.449313   0.339143        0           1  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = pd.concat([X_train_categ, X_train_num, train['holiday'], train['workingday']], axis=1, ignore_index=False)\n",
    "print('X_train: ', X_train.shape)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>...</th>\n",
       "      <th>weathersit_2</th>\n",
       "      <th>weathersit_3</th>\n",
       "      <th>temp</th>\n",
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       "      <th>windspeed</th>\n",
       "      <th>holiday</th>\n",
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       "      <th>cnt</th>\n",
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       "      <th>0</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
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       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.209120</td>\n",
       "      <td>0.197158</td>\n",
       "      <td>0.449313</td>\n",
       "      <td>0.339143</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant  season_1  season_2  season_3  season_4  mnth_1  mnth_2  mnth_3  \\\n",
       "0        1         1         0         0         0       1       0       0   \n",
       "1        2         1         0         0         0       1       0       0   \n",
       "2        3         1         0         0         0       1       0       0   \n",
       "3        4         1         0         0         0       1       0       0   \n",
       "4        5         1         0         0         0       1       0       0   \n",
       "\n",
       "   mnth_4  mnth_5  ...  weathersit_2  weathersit_3      temp     atemp  \\\n",
       "0       0       0  ...             1             0  0.355170  0.373517   \n",
       "1       0       0  ...             1             0  0.379232  0.360541   \n",
       "2       0       0  ...             0             0  0.171000  0.144830   \n",
       "3       0       0  ...             0             0  0.175530  0.174649   \n",
       "4       0       0  ...             0             0  0.209120  0.197158   \n",
       "\n",
       "        hum  windspeed  holiday  workingday  yr   cnt  \n",
       "0  0.828620   0.284606        0           0   0   985  \n",
       "1  0.715771   0.466215        0           0   0   801  \n",
       "2  0.449638   0.465740        0           1   0  1349  \n",
       "3  0.607131   0.284297        0           1   0  1562  \n",
       "4  0.449313   0.339143        0           1   0  1600  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FE_train = pd.concat([train['instant'], X_train, train['yr'], train['cnt']], axis = 1)\n",
    "#FE_train.to_csv('FE_day.csv', index=False)\n",
    "FE_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 35 columns):\n",
      "instant         731 non-null int64\n",
      "season_1        731 non-null uint8\n",
      "season_2        731 non-null uint8\n",
      "season_3        731 non-null uint8\n",
      "season_4        731 non-null uint8\n",
      "mnth_1          731 non-null uint8\n",
      "mnth_2          731 non-null uint8\n",
      "mnth_3          731 non-null uint8\n",
      "mnth_4          731 non-null uint8\n",
      "mnth_5          731 non-null uint8\n",
      "mnth_6          731 non-null uint8\n",
      "mnth_7          731 non-null uint8\n",
      "mnth_8          731 non-null uint8\n",
      "mnth_9          731 non-null uint8\n",
      "mnth_10         731 non-null uint8\n",
      "mnth_11         731 non-null uint8\n",
      "mnth_12         731 non-null uint8\n",
      "weekday_0       731 non-null uint8\n",
      "weekday_1       731 non-null uint8\n",
      "weekday_2       731 non-null uint8\n",
      "weekday_3       731 non-null uint8\n",
      "weekday_4       731 non-null uint8\n",
      "weekday_5       731 non-null uint8\n",
      "weekday_6       731 non-null uint8\n",
      "weathersit_1    731 non-null uint8\n",
      "weathersit_2    731 non-null uint8\n",
      "weathersit_3    731 non-null uint8\n",
      "temp            731 non-null float64\n",
      "atemp           731 non-null float64\n",
      "hum             731 non-null float64\n",
      "windspeed       731 non-null float64\n",
      "holiday         731 non-null int64\n",
      "workingday      731 non-null int64\n",
      "yr              731 non-null int64\n",
      "cnt             731 non-null int64\n",
      "dtypes: float64(4), int64(5), uint8(26)\n",
      "memory usage: 70.0 KB\n"
     ]
    }
   ],
   "source": [
    "FE_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.拆分数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df = pd.read_csv('FE_day.csv')\n",
    "# 显示数据的前五行\n",
    "#df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 从处理好的特征数据集中分离出特征集X和输出集y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = FE_train['cnt']\n",
    "X = FE_train.drop(['cnt'], axis=1)\n",
    "# 保存拆分后的特征名称\n",
    "x_feats_name = X.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 将数据分割为训练数据集，以及测试数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.训练模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.1 训练最小二乘模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>特征项</th>\n",
       "      <th>权重</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>yr</td>\n",
       "      <td>4550.708897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>temp</td>\n",
       "      <td>2654.792827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>mnth_9</td>\n",
       "      <td>1287.992360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>atemp</td>\n",
       "      <td>995.293778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>mnth_10</td>\n",
       "      <td>929.887649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>weathersit_1</td>\n",
       "      <td>914.410909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>season_4</td>\n",
       "      <td>830.579518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>mnth_12</td>\n",
       "      <td>586.296405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>mnth_8</td>\n",
       "      <td>517.803038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>weathersit_2</td>\n",
       "      <td>409.589079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>mnth_11</td>\n",
       "      <td>407.138803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>weekday_6</td>\n",
       "      <td>238.417312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>workingday</td>\n",
       "      <td>216.956549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mnth_6</td>\n",
       "      <td>189.797216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>season_2</td>\n",
       "      <td>85.141889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>weekday_5</td>\n",
       "      <td>77.516263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>weekday_3</td>\n",
       "      <td>66.464787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>weekday_4</td>\n",
       "      <td>43.650546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mnth_5</td>\n",
       "      <td>5.009200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>instant</td>\n",
       "      <td>-6.919060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>weekday_2</td>\n",
       "      <td>-31.943212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>season_3</td>\n",
       "      <td>-130.807162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>weekday_0</td>\n",
       "      <td>-191.612446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>weekday_1</td>\n",
       "      <td>-202.493250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>mnth_7</td>\n",
       "      <td>-203.137582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>holiday</td>\n",
       "      <td>-263.761415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mnth_4</td>\n",
       "      <td>-485.714239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mnth_3</td>\n",
       "      <td>-541.439917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>season_1</td>\n",
       "      <td>-784.914245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>-1174.845790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>mnth_2</td>\n",
       "      <td>-1207.111892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>hum</td>\n",
       "      <td>-1298.592138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>weathersit_3</td>\n",
       "      <td>-1323.999988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mnth_1</td>\n",
       "      <td>-1486.521042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             特征项           权重\n",
       "33            yr  4550.708897\n",
       "27          temp  2654.792827\n",
       "13        mnth_9  1287.992360\n",
       "28         atemp   995.293778\n",
       "14       mnth_10   929.887649\n",
       "24  weathersit_1   914.410909\n",
       "4       season_4   830.579518\n",
       "16       mnth_12   586.296405\n",
       "12        mnth_8   517.803038\n",
       "25  weathersit_2   409.589079\n",
       "15       mnth_11   407.138803\n",
       "23     weekday_6   238.417312\n",
       "32    workingday   216.956549\n",
       "10        mnth_6   189.797216\n",
       "2       season_2    85.141889\n",
       "22     weekday_5    77.516263\n",
       "20     weekday_3    66.464787\n",
       "21     weekday_4    43.650546\n",
       "9         mnth_5     5.009200\n",
       "0        instant    -6.919060\n",
       "19     weekday_2   -31.943212\n",
       "3       season_3  -130.807162\n",
       "17     weekday_0  -191.612446\n",
       "18     weekday_1  -202.493250\n",
       "11        mnth_7  -203.137582\n",
       "31       holiday  -263.761415\n",
       "8         mnth_4  -485.714239\n",
       "7         mnth_3  -541.439917\n",
       "1       season_1  -784.914245\n",
       "30     windspeed -1174.845790\n",
       "6         mnth_2 -1207.111892\n",
       "29           hum -1298.592138\n",
       "26  weathersit_3 -1323.999988\n",
       "5         mnth_1 -1486.521042"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred_lr = lr.predict(X_train)\n",
    "y_test_pred_lr = lr.predict(X_test)\n",
    "\n",
    "lr_w = pd.DataFrame({'特征项':list(x_feats_name), '权重':list(lr.coef_.T)})\n",
    "lr_w.sort_values(by=['权重'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.1.1 评价最小二乘模型（RMSE & R2）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最小二乘模型在训练集上（Train）\n",
      "\t均方根误差为 742.7543512758712\n",
      "\tR2为 0.8516480637403496\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "import math\n",
    "print('最小二乘模型在训练集上（Train）')\n",
    "print('\\t均方根误差为',math.sqrt(mean_squared_error(y_train, y_train_pred_lr)))\n",
    "print('\\tR2为',r2_score(y_train, y_train_pred_lr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最小二乘模型在测试集上（Test）\n",
      "\t均方根误差为 814.4749076863648\n",
      "\tR2为 0.8279474225980328\n"
     ]
    }
   ],
   "source": [
    "print('最小二乘模型在测试集上（Test）')\n",
    "print('\\t均方根误差为',math.sqrt(mean_squared_error(y_test, y_test_pred_lr)))\n",
    "print('\\tR2为',r2_score(y_test, y_test_pred_lr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.2训练岭回归模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>特征项</th>\n",
       "      <th>ridge权重</th>\n",
       "      <th>lr权重</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>temp</td>\n",
       "      <td>1778.493414</td>\n",
       "      <td>2654.792827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>atemp</td>\n",
       "      <td>1546.374034</td>\n",
       "      <td>995.293778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>yr</td>\n",
       "      <td>1504.623924</td>\n",
       "      <td>4550.708897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>weathersit_1</td>\n",
       "      <td>914.843092</td>\n",
       "      <td>914.410909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>season_4</td>\n",
       "      <td>767.205317</td>\n",
       "      <td>830.579518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>mnth_9</td>\n",
       "      <td>678.352931</td>\n",
       "      <td>1287.992360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>weathersit_2</td>\n",
       "      <td>388.865215</td>\n",
       "      <td>409.589079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mnth_5</td>\n",
       "      <td>387.713628</td>\n",
       "      <td>5.009200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mnth_6</td>\n",
       "      <td>369.663154</td>\n",
       "      <td>189.797216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mnth_3</td>\n",
       "      <td>298.550050</td>\n",
       "      <td>-541.439917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>weekday_6</td>\n",
       "      <td>227.515740</td>\n",
       "      <td>238.417312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>workingday</td>\n",
       "      <td>212.558710</td>\n",
       "      <td>216.956549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>mnth_8</td>\n",
       "      <td>205.686009</td>\n",
       "      <td>517.803038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>season_2</td>\n",
       "      <td>110.998614</td>\n",
       "      <td>85.141889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mnth_4</td>\n",
       "      <td>106.306513</td>\n",
       "      <td>-485.714239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>mnth_10</td>\n",
       "      <td>84.873020</td>\n",
       "      <td>929.887649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>weekday_5</td>\n",
       "      <td>81.396550</td>\n",
       "      <td>77.516263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>weekday_4</td>\n",
       "      <td>62.795850</td>\n",
       "      <td>43.650546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>weekday_3</td>\n",
       "      <td>59.857933</td>\n",
       "      <td>66.464787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>instant</td>\n",
       "      <td>1.417157</td>\n",
       "      <td>-6.919060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>weekday_2</td>\n",
       "      <td>-34.140080</td>\n",
       "      <td>-31.943212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>season_3</td>\n",
       "      <td>-91.388275</td>\n",
       "      <td>-130.807162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>mnth_2</td>\n",
       "      <td>-143.125249</td>\n",
       "      <td>-1207.111892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>weekday_0</td>\n",
       "      <td>-191.851510</td>\n",
       "      <td>-191.612446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mnth_1</td>\n",
       "      <td>-194.714350</td>\n",
       "      <td>-1486.521042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>weekday_1</td>\n",
       "      <td>-205.574484</td>\n",
       "      <td>-202.493250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>mnth_7</td>\n",
       "      <td>-242.548582</td>\n",
       "      <td>-203.137582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>holiday</td>\n",
       "      <td>-248.222941</td>\n",
       "      <td>-263.761415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>mnth_11</td>\n",
       "      <td>-725.828529</td>\n",
       "      <td>407.138803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>season_1</td>\n",
       "      <td>-786.815656</td>\n",
       "      <td>-784.914245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>mnth_12</td>\n",
       "      <td>-824.928596</td>\n",
       "      <td>586.296405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>-1087.773198</td>\n",
       "      <td>-1174.845790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>hum</td>\n",
       "      <td>-1142.397276</td>\n",
       "      <td>-1298.592138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>weathersit_3</td>\n",
       "      <td>-1303.708307</td>\n",
       "      <td>-1323.999988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             特征项      ridge权重         lr权重\n",
       "27          temp  1778.493414  2654.792827\n",
       "28         atemp  1546.374034   995.293778\n",
       "33            yr  1504.623924  4550.708897\n",
       "24  weathersit_1   914.843092   914.410909\n",
       "4       season_4   767.205317   830.579518\n",
       "13        mnth_9   678.352931  1287.992360\n",
       "25  weathersit_2   388.865215   409.589079\n",
       "9         mnth_5   387.713628     5.009200\n",
       "10        mnth_6   369.663154   189.797216\n",
       "7         mnth_3   298.550050  -541.439917\n",
       "23     weekday_6   227.515740   238.417312\n",
       "32    workingday   212.558710   216.956549\n",
       "12        mnth_8   205.686009   517.803038\n",
       "2       season_2   110.998614    85.141889\n",
       "8         mnth_4   106.306513  -485.714239\n",
       "14       mnth_10    84.873020   929.887649\n",
       "22     weekday_5    81.396550    77.516263\n",
       "21     weekday_4    62.795850    43.650546\n",
       "20     weekday_3    59.857933    66.464787\n",
       "0        instant     1.417157    -6.919060\n",
       "19     weekday_2   -34.140080   -31.943212\n",
       "3       season_3   -91.388275  -130.807162\n",
       "6         mnth_2  -143.125249 -1207.111892\n",
       "17     weekday_0  -191.851510  -191.612446\n",
       "5         mnth_1  -194.714350 -1486.521042\n",
       "18     weekday_1  -205.574484  -202.493250\n",
       "11        mnth_7  -242.548582  -203.137582\n",
       "31       holiday  -248.222941  -263.761415\n",
       "15       mnth_11  -725.828529   407.138803\n",
       "1       season_1  -786.815656  -784.914245\n",
       "16       mnth_12  -824.928596   586.296405\n",
       "30     windspeed -1087.773198 -1174.845790\n",
       "29           hum -1142.397276 -1298.592138\n",
       "26  weathersit_3 -1303.708307 -1323.999988"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import RidgeCV\n",
    "\n",
    "alphas = [0.01, 0.1, 1, 10, 100]\n",
    "ridge = RidgeCV(alphas=alphas)\n",
    "ridge.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "\n",
    "ridge_w = pd.DataFrame({'特征项':list(x_feats_name), 'ridge权重':list(ridge.coef_.T), 'lr权重':list(lr.coef_.T)})\n",
    "ridge_w.sort_values(by=['ridge权重'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.2.1 评价岭回归模型（RMSE & R2）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "岭回归模型在训练集上（Train）\n",
      "\t均方根误差为 747.4410131599844\n",
      "\tR2为 0.8497700029725632\n"
     ]
    }
   ],
   "source": [
    "print('岭回归模型在训练集上（Train）')\n",
    "print('\\t均方根误差为',math.sqrt(mean_squared_error(y_train, y_train_pred_ridge)))\n",
    "print('\\tR2为',r2_score(y_train, y_train_pred_ridge))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "岭回归模型在训练集上（Test）\n",
      "\t均方根误差为 812.0412685702523\n",
      "\tR2为 0.8289740676744334\n"
     ]
    }
   ],
   "source": [
    "print('岭回归模型在训练集上（Test）')\n",
    "print('\\t均方根误差为',math.sqrt(mean_squared_error(y_test, y_test_pred_ridge)))\n",
    "print('\\tR2为',r2_score(y_test, y_test_pred_ridge))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.3 训练Lasso模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Develop Kit\\Anaconda3_2019\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "D:\\Develop Kit\\Anaconda3_2019\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "D:\\Develop Kit\\Anaconda3_2019\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "D:\\Develop Kit\\Anaconda3_2019\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "D:\\Develop Kit\\Anaconda3_2019\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "D:\\Develop Kit\\Anaconda3_2019\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "D:\\Develop Kit\\Anaconda3_2019\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "D:\\Develop Kit\\Anaconda3_2019\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "D:\\Develop Kit\\Anaconda3_2019\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "D:\\Develop Kit\\Anaconda3_2019\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>特征项</th>\n",
       "      <th>lasso权重</th>\n",
       "      <th>ridge权重</th>\n",
       "      <th>lr权重</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>yr</td>\n",
       "      <td>3472.540499</td>\n",
       "      <td>1504.623924</td>\n",
       "      <td>4550.708897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>temp</td>\n",
       "      <td>2609.975567</td>\n",
       "      <td>1778.493414</td>\n",
       "      <td>2654.792827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>atemp</td>\n",
       "      <td>1031.599947</td>\n",
       "      <td>1546.374034</td>\n",
       "      <td>995.293778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>mnth_9</td>\n",
       "      <td>924.519702</td>\n",
       "      <td>678.352931</td>\n",
       "      <td>1287.992360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>season_4</td>\n",
       "      <td>759.982539</td>\n",
       "      <td>767.205317</td>\n",
       "      <td>830.579518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>weathersit_1</td>\n",
       "      <td>499.555091</td>\n",
       "      <td>914.843092</td>\n",
       "      <td>914.410909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>mnth_10</td>\n",
       "      <td>472.973717</td>\n",
       "      <td>84.873020</td>\n",
       "      <td>929.887649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>mnth_8</td>\n",
       "      <td>244.553549</td>\n",
       "      <td>205.686009</td>\n",
       "      <td>517.803038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mnth_6</td>\n",
       "      <td>99.295175</td>\n",
       "      <td>369.663154</td>\n",
       "      <td>189.797216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>weekday_5</td>\n",
       "      <td>28.801451</td>\n",
       "      <td>81.396550</td>\n",
       "      <td>77.516263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>workingday</td>\n",
       "      <td>26.288122</td>\n",
       "      <td>212.558710</td>\n",
       "      <td>216.956549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>weekday_3</td>\n",
       "      <td>17.401149</td>\n",
       "      <td>59.857933</td>\n",
       "      <td>66.464787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mnth_5</td>\n",
       "      <td>8.050132</td>\n",
       "      <td>387.713628</td>\n",
       "      <td>5.009200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>season_2</td>\n",
       "      <td>5.856632</td>\n",
       "      <td>110.998614</td>\n",
       "      <td>85.141889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>weekday_6</td>\n",
       "      <td>1.222883</td>\n",
       "      <td>227.515740</td>\n",
       "      <td>238.417312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>weathersit_2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>388.865215</td>\n",
       "      <td>409.589079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>weekday_4</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>62.795850</td>\n",
       "      <td>43.650546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>instant</td>\n",
       "      <td>-3.972549</td>\n",
       "      <td>1.417157</td>\n",
       "      <td>-6.919060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>mnth_12</td>\n",
       "      <td>-45.203860</td>\n",
       "      <td>-824.928596</td>\n",
       "      <td>586.296405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>weekday_2</td>\n",
       "      <td>-79.989619</td>\n",
       "      <td>-34.140080</td>\n",
       "      <td>-31.943212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>mnth_11</td>\n",
       "      <td>-141.660225</td>\n",
       "      <td>-725.828529</td>\n",
       "      <td>407.138803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>season_3</td>\n",
       "      <td>-199.683869</td>\n",
       "      <td>-91.388275</td>\n",
       "      <td>-130.807162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>weekday_1</td>\n",
       "      <td>-249.463453</td>\n",
       "      <td>-205.574484</td>\n",
       "      <td>-202.493250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mnth_3</td>\n",
       "      <td>-358.415759</td>\n",
       "      <td>298.550050</td>\n",
       "      <td>-541.439917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>mnth_7</td>\n",
       "      <td>-386.246331</td>\n",
       "      <td>-242.548582</td>\n",
       "      <td>-203.137582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mnth_4</td>\n",
       "      <td>-392.886936</td>\n",
       "      <td>106.306513</td>\n",
       "      <td>-485.714239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>weekday_0</td>\n",
       "      <td>-427.432845</td>\n",
       "      <td>-191.851510</td>\n",
       "      <td>-191.612446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>holiday</td>\n",
       "      <td>-446.898429</td>\n",
       "      <td>-248.222941</td>\n",
       "      <td>-263.761415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>season_1</td>\n",
       "      <td>-864.040406</td>\n",
       "      <td>-786.815656</td>\n",
       "      <td>-784.914245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>mnth_2</td>\n",
       "      <td>-938.565103</td>\n",
       "      <td>-143.125249</td>\n",
       "      <td>-1207.111892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mnth_1</td>\n",
       "      <td>-1130.691382</td>\n",
       "      <td>-194.714350</td>\n",
       "      <td>-1486.521042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>-1180.476252</td>\n",
       "      <td>-1087.773198</td>\n",
       "      <td>-1174.845790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>hum</td>\n",
       "      <td>-1317.015025</td>\n",
       "      <td>-1142.397276</td>\n",
       "      <td>-1298.592138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>weathersit_3</td>\n",
       "      <td>-1730.522858</td>\n",
       "      <td>-1303.708307</td>\n",
       "      <td>-1323.999988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             特征项      lasso权重      ridge权重         lr权重\n",
       "33            yr  3472.540499  1504.623924  4550.708897\n",
       "27          temp  2609.975567  1778.493414  2654.792827\n",
       "28         atemp  1031.599947  1546.374034   995.293778\n",
       "13        mnth_9   924.519702   678.352931  1287.992360\n",
       "4       season_4   759.982539   767.205317   830.579518\n",
       "24  weathersit_1   499.555091   914.843092   914.410909\n",
       "14       mnth_10   472.973717    84.873020   929.887649\n",
       "12        mnth_8   244.553549   205.686009   517.803038\n",
       "10        mnth_6    99.295175   369.663154   189.797216\n",
       "22     weekday_5    28.801451    81.396550    77.516263\n",
       "32    workingday    26.288122   212.558710   216.956549\n",
       "20     weekday_3    17.401149    59.857933    66.464787\n",
       "9         mnth_5     8.050132   387.713628     5.009200\n",
       "2       season_2     5.856632   110.998614    85.141889\n",
       "23     weekday_6     1.222883   227.515740   238.417312\n",
       "25  weathersit_2     0.000000   388.865215   409.589079\n",
       "21     weekday_4    -0.000000    62.795850    43.650546\n",
       "0        instant    -3.972549     1.417157    -6.919060\n",
       "16       mnth_12   -45.203860  -824.928596   586.296405\n",
       "19     weekday_2   -79.989619   -34.140080   -31.943212\n",
       "15       mnth_11  -141.660225  -725.828529   407.138803\n",
       "3       season_3  -199.683869   -91.388275  -130.807162\n",
       "18     weekday_1  -249.463453  -205.574484  -202.493250\n",
       "7         mnth_3  -358.415759   298.550050  -541.439917\n",
       "11        mnth_7  -386.246331  -242.548582  -203.137582\n",
       "8         mnth_4  -392.886936   106.306513  -485.714239\n",
       "17     weekday_0  -427.432845  -191.851510  -191.612446\n",
       "31       holiday  -446.898429  -248.222941  -263.761415\n",
       "1       season_1  -864.040406  -786.815656  -784.914245\n",
       "6         mnth_2  -938.565103  -143.125249 -1207.111892\n",
       "5         mnth_1 -1130.691382  -194.714350 -1486.521042\n",
       "30     windspeed -1180.476252 -1087.773198 -1174.845790\n",
       "29           hum -1317.015025 -1142.397276 -1298.592138\n",
       "26  weathersit_3 -1730.522858 -1303.708307 -1323.999988"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LassoCV\n",
    "alphas = [0.01, 0.1, 1, 10, 100]\n",
    "lasso = LassoCV(alphas=alphas)\n",
    "lasso.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred_lasso = lr.predict(X_train)\n",
    "y_test_pred_lasso = lr.predict(X_test)\n",
    "\n",
    "lasso_w = pd.DataFrame({'特征项':list(x_feats_name), 'lasso权重':list(lasso.coef_.T), 'ridge权重':list(ridge.coef_.T), 'lr权重':list(lr.coef_.T)})\n",
    "lasso_w.sort_values(by=['lasso权重'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.3.1 评价Lasso模型（RMSE & R2）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso模型在训练集上（Train）\n",
      "\t均方根误差为 742.7543512758712\n",
      "\tR2为 0.8516480637403496\n"
     ]
    }
   ],
   "source": [
    "print('Lasso模型在训练集上（Train）')\n",
    "print('\\t均方根误差为',math.sqrt(mean_squared_error(y_train, y_train_pred_lasso)))\n",
    "print('\\tR2为',r2_score(y_train, y_train_pred_lasso))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "岭回归模型在训练集上（Test）\n",
      "\t均方根误差为 814.4749076863648\n",
      "\tR2为 0.8279474225980328\n"
     ]
    }
   ],
   "source": [
    "print('岭回归模型在训练集上（Test）')\n",
    "print('\\t均方根误差为',math.sqrt(mean_squared_error(y_test, y_test_pred_lasso)))\n",
    "print('\\tR2为',r2_score(y_test, y_test_pred_lasso))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过比较上述三种模型得到的特征系数可以看出，岭回归和Lasso回归都能使得线性回归权重系数收缩，并且在Lasso中有的特征参数系数为0。回归系数都收缩是原因岭回归和Lasso都在最小二乘线性回归的基础上加了正则，限制了特征参数的取值，而Lasso中某些特征的系数为0，是因为对于L1正则，目标函数求的是次梯度，当梯度在次梯度集合内的时候，该维度的特征系数为0\n",
    "\n",
    "通过观察模型评价指标可以看出，在训练集上评价最好的是岭回归模型，其次是Lasso模型，最后是最小二乘线性回归。原因是岭回归和Lasso都在最小二乘线性回归模型中加入了正则项，防止了模型过拟合的问题，所以效果要更好些。而在特征分析中，我们看到有很多特征相关性比较大，比如说温度与体感温度，在特征多，且特征间存在共线性关系时使用L2正则效果要更好，所以这这里岭回归模型比Lasso回归又好一点点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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